research-article

Multi-view Multi-aspect Neural Networks for Next-basket Recommendation

Authors:

Guohui LiAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1283 - 1292

https://doi.org/10.1145/3539618.3591738

Published: 18 July 2023 Publication History

Abstract

Next-basket recommendation (NBR) is a type of recommendation that aims to recommend a set of items to users according to their historical basket sequences. Existing NBR methods suffer from two limitations: (1) overlooking low-level item correlations, which results in coarse-grained item representation; and (2) failing to consider spurious interests in repeated behaviors, leading to suboptimal user interest learning. To address these limitations, we propose a novel solution named Multi-view Multi-aspect Neural Recommendation (MMNR) for NBR, which first normalizes the interactions from both the user-side and item-side, respectively, aiming to remove the spurious interests, and utilizes them as weights for items from different views to construct differentiated representations for each interaction item, enabling comprehensive user interest learning. Then, to capture low-level item correlations, MMNR models different aspects of items to obtain disentangled representations of items, thereby fully capturing multiple user interests. Extensive experiments on real-world datasets demonstrate the effectiveness of MMNR, showing that it consistently outperforms several state-of-the-art NBR methods.

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Cited By

Feng ZWang PLi KLi CWang SAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Contextual MAB Oriented Embedding Denoising for Sequential RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635798(199-207)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635798
Ma LLi YMai ZLiang FWang CChen MGuizani M(2024)Cross-Store Next-Basket Recommendation2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00037(301-310)Online publication date: 9-Dec-2024
https://doi.org/10.1109/ICDM59182.2024.00037
Chen QLi JGuo ZLi GDeng ZFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Attribute-enhanced Dual Channel Representation Learning for Session-based RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615245(3793-3797)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615245

Index Terms

Multi-view Multi-aspect Neural Networks for Next-basket Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Generative Next-Basket Recommendation
RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Next-basket Recommendation (NBR) refers to the task of predicting a set of items that a user will purchase in the next basket. However, most of existing works merely focus on the correlations between user preferences and predicted items, ignoring the ...
Modeling Personalized Item Frequency Information for Next-basket Recommendation
SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. NBR is in ...
Incorporating Co-purchase Correlation for Next-basket Recommendation
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Next-basket recommendation (NBR) aims to recommend a set of items that users would most likely purchase together. Existing approaches use deep learning to capture basket-level preference and traditional statistical methods to model user behavior ...

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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Published: 18 July 2023

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National Natural Science Foundation of China

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SIGIR '23

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SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Cited By

Feng ZWang PLi KLi CWang SAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Contextual MAB Oriented Embedding Denoising for Sequential RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635798(199-207)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635798
Ma LLi YMai ZLiang FWang CChen MGuizani M(2024)Cross-Store Next-Basket Recommendation2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00037(301-310)Online publication date: 9-Dec-2024
https://doi.org/10.1109/ICDM59182.2024.00037
Chen QLi JGuo ZLi GDeng ZFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Attribute-enhanced Dual Channel Representation Learning for Session-based RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615245(3793-3797)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615245

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